Medicare

From Invisible to Enrolled: How Artificial Intelligence Is Reshaping Clinical Trial Recruitment.

Clinical trial recruitment continues to fail at scale, with eligible patients often invisible to routine screening despite being present in electronic health records. Disease stage, treatment response, biomarker results, and clinical reasoning are frequently documented in narrative form rather than structured fields, placing eligibility-relevant information beyond the reach of conventional recruit...

Long-PVSUNet: A Longitudinal Deep Learning Framework for Count-Oriented Perivascular Space Segmentation in Brain MRI.

Enlarged perivascular spaces (ePVS) are small, sparse MRI-visible markers of cerebral small vessel disease and brain ageing. Their size, low contrast, and severe foreground-background imbalance make automated segmentation challenging. Existing methods are mainly cross-sectional and do not model temporal consistency, limiting their utility for tracking longitudinal change. We developed Long-PVSUNet...

What Platform Scores Miss: Multidimensional Evaluation of AI Teaching Agents in Medical Education.

BACKGROUND: Large language model (LLM)-based AI teaching agents are increasingly used in medical education, yet their pedagogical quality is typically...

Implementation of an opioid use disorder (OUD) machine-learning phenotype in real-time for the ADAPT clinical trial.

We developed and deploy a real‑time, electronic health record‑integrated machine learning phenotype to identify emergency department patients with opi...

Improving portability of knowledge-based planning using an LLM-driven plan refinement framework in lung radiotherapy.

BACKGROUND: Knowledge-based planning (KBP) has improved the quality and efficiency of radiotherapy treatment planning. However, its broader clinical a...

Beam geometry optimization and convergence mode evaluation for knowledge-based planning of hippocampal avoidance whole-brain radiotherapy: A cross-platform study with quantitative scorecard assessment.

BACKGROUND: Hippocampal avoidance whole-brain radiotherapy (HA-WBRT) is used to treat brain metastases while preserving cognitive function by sparing ...

Who is responsible? A risk-stratified framework for pharmacist accountability in artificial intelligence-assisted managed care.

Artificial intelligence (AI)-enabled tools are increasingly integrated into managed care pharmacy workflows, particularly in utilization management an...

Molecules to market: A probabilistic artificial intelligence framework for predicting managed care adoption at the level of drug discovery.

Artificial intelligence (AI) is rapidly compressing the timeline from molecular discovery to regulatory submission, meaning pipeline therapies will re...

Artificial intelligence-enabled causal estimate of Medicare drug plan integration in cancer care: A doubly robust machine learning instrumental variable analysis.

BACKGROUND: Artificial intelligence (AI) methods are increasingly used to strengthen policy evaluation in managed care pharmacy. Among Medicare benefi...

A pharmacist-overseen, artificial intelligence-enabled model for provider-side prior authorization: From burden to opportunity (PAVE-1).

Prior authorization (PA) imposes substantial administrative burdens on clinicians, contributing to burnout, delayed care, and excess health care spend...

Multi-modal direct LINAC parameter prediction for pancreatic VMAT: An optimization-free approach.

BACKGROUND: Pancreatic cancer Volumetric Modulated Arc Therapy (VMAT) planning presents a significant dosimetric challenge due to the high-dose gradie...

Large Language Models vs. NASS Common Coding Scenarios: An Analysis of Accuracy and Financial Implications using 2026 CPT AMA Codebook Guidelines.

BACKGROUND CONTEXT: Accurate Current Procedural Terminology (CPT) coding is essential for compliant revenue cycle management in spine surgery. However...

Artificial Intelligence-Enabled Acquisition and Interpretation for Screening Aortic Stenosis.

IMPORTANCE: Timely identification of aortic stenosis (AS) is essential for appropriate clinical management, yet screening remains limited by dependenc...

Risks of Major Adverse Kidney Events in Non-Hispanic Black Patients with Diabetes or Hypertension: A real-world cohort study.

INTRODUCTION: Chronic kidney disease (CKD) disproportionately burdens non-Hispanic Black (NHB) patients who experience a three- to four-fold higher ri...

ASPIRE: Accurate alternative splicing prediction from limited RNA sequencing data and a minimal gene set.

Alternative splicing is a fundamental biological mechanism that increases protein diversity and regulates critical cellular processes across eukaryote...

Advancing waste-to-energy systems in institutional settings: Machine learning models for sustainable energy recovery from municipal solid waste at university campuses.

Municipal solid waste (MSW) generation in university campuses across sub-Saharan Africa presents a critical management challenge and an underutilised ...

Uncertainty quantification using conformal prediction for mesh-based simulations.

Machine learning surrogates for computational fluid dynamics (CFD) achieve substantial speedups but lack uncertainty quantification (UQ). We develop a...

Uncertainty quantification in train delay prediction with conformal prediction.

Predicting train delays is crucial for railway operations and passenger experience, but point predictions fail to capture uncertainty, limiting their ...

AMR-VSF: an adaptive memory correction framework for robust 3D CT aorta segmentation.

PURPOSE: Accurate 3D aortic segmentation in CT images is vital for cardiovascular disease diagnosis, surgical planning, and intraoperative navigation....

Digital Oral Health beyond Professional Dental Care Settings: A Scoping Review of Technologies and Applications from Prevention to Early Detection.

BACKGROUND: Oral non-communicable diseases impose a substantial and unequally distributed global burden. Digital technologies have the potential to su...